lasso {inet} | R Documentation |
Estimate GGM with nodewise regression and the lasso.
Description
Estimate a Gaussian Graphical Model with lasso-regularized nodewise regression, where the regularization parameter is selected with cross-validation. This is a wrapper around the function cv.glmnet()
from the glmnet
package.
Usage
lasso(data, pbar = TRUE, nfolds = 10, rulereg = "and")
Arguments
data |
An n x p matrix containing the data, where n are cases and p are variables |
pbar |
If |
nfolds |
Specifies the number of folds used to select the regularization parameter in each of the p nodewise regressions. |
rulereg |
Specifies how parameter estimates should be combined across nodewise regressions. The options are the AND-rule (requiring both estimates to be significant) or the OR-rule (only requiring one estimate to be significant). Defaults to |
Value
The function returns a list with the following entries:
est |
A p x p matrix with point estimates for all partial correlations |
select |
A p x p indicator matrix indicating which edges have been selected to be present. |
ints |
A p-vector of estimated intercepts. |
Author(s)
Jonas Haslbeck <jonashaslbeck@gmail.com>
References
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.
Examples
# Toy example that runs relatively quickly
library(MASS)
p <- 5 # number of variables
data <- mvrnorm(n=100, mu=rep(0, p), Sigma = diag(p))
set.seed(1)
out <- lasso(data = data)
## Not run:
# Fit GGM to PTSD data
set.seed(1)
out <- lasso(data = ptsd_data)
## End(Not run)